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crossrefMachine Learning and Knowledge Extraction2025-07-25Cited by 1

VisRep: Towards an Automated, Reflective AI System for Documenting Visualisation Design Processes

Aron E. Owen, Jonathan C. Roberts

VisRep (Visualisation Report) is an AI-powered system for capturing and structuring the early stages of the visualisation design process. It addresses a critical gap in predesign: the lack of tools that can naturally record, organise, and transform raw ideation, spoken thoughts, sketches, and evolving concepts into polished, shareable outputs. Users engage in talk-aloud sessions through a terminal-style interface supported by intelligent transcription and eleven structured questions that frame intent, audience, and output goals. These inputs are then processed by a large language model (LLM) guided by markdown-based output templates for reports, posters, and slides. The system aligns free-form ideas with structured communication using prompt engineering to ensure clarity, coherence, and visual consistency. VisRep not only automates the generation of professional deliverables but also enhances reflective practice by bridging spontaneous ideation and structured documentation. This paper introduces VisRep’s methodology, interface design, and AI-driven workflow, demonstrating how it improves the fidelity and transparency of the visualisation design process across academic, professional, and creative domains.

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Financial institutions are increasingly turning to artificial intelligence (AI) to improve their decision-making processes and gain a competitive edge. Due to the iterative process of AI development, it is mandatory to have a structured process in place, from the design to the de…

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crossrefMachine Learning and Knowledge Extraction2026-06-18

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crossrefMachine Learning and Knowledge Extraction2024-02-05Cited by 47

More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts

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Modern AI applications have caused broad societal implications across key public domains. While previous research primarily focuses on individual user perspectives regarding AI systems, this study expands our understanding to encompass general public perceptions. Through a survey…

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crossrefMachine Learning and Knowledge Extraction2026-05-08

Controlled Agentic AI Systems: A Governance-Driven Architecture for Auditable and Reproducible Decision Pipelines

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Artificial intelligence systems deployed in safety-critical and regulated environments require not only predictive performance, but also strict adherence to operational constraints, auditability, and reproducibility. However, in most contemporary architectures, governance is trea…

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